Self-Supervised Learning of Structured Dynamics from Videos
- 类型：arxiv
- 标识：2607.21576
- 链接：https://arxiv.org/abs/2607.21576
- 主分类：multimodal
- 形态：position
- 被引：0
- 被引来源：Semantic Scholar
- S2被引：0
- 影响力被引：0
- TLDR：The Structured Dynamics Model (SDM) is proposed, which explicitly separates the dominant source of temporal change from residual dynamics through future-feature prediction, rather than representing video change with a single entangled latent or with unstructured, spatially dense transition tokens.
- 待LLM分类：否
- 标题中文：从视频中自监督学习结构化动态
- TLDR中文：提出结构化动态模型（SDM），通过未来特征预测，显式地将时间变化的主导来源与残差动态分离开来，而非使用单一纠缠的隐变量或非结构化的、空间密集的转移 token 来表示视频变化。
- 来源文件：
- /inbox/tom/_candidates/2026-07-24-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-25-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-26-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-27-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-27-agent-memory-tool-use-candidates.json
- [S2 enrich]